Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain : Proceedings of the Frontis Workshop on Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain, held in Wagening, The Netherlands, 1-14 May 2003

Van Boekel, M. A. J. S. (EDT); Stein, A. (EDT); Van Bruggen, A. H. C. (EDT)

ISBN 10: 1402019173 / ISBN 13: 9781402019173

Published by Springer, 2004

About this Item

Unread book in perfect condition.

Seller Inventory # 2300130

Bibliographic Details

• Title: Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain : Proceedings of the Frontis Workshop on Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain, held in Wagening, The Netherlands, 1-14 May 2003
• Publisher: Springer
• Publication Date: 2004
• Language: English
• ISBN 10: 1402019173
• ISBN 13: 9781402019173
• Binding: Soft cover
• Condition: As New

About this title

Synopsis

The food market is changing from a producer-controlled to a consumer-directed market. A main driving force is consumer concern about agricultural production methods and food safety. More than before, the consumer demands transparency of the production and processing chain.
A food chain can be quite complex and the use of models has become indispensable to handle this complexity. Modelling tools are becoming increasingly important to guide the decisions for production of high-quality and safe agricultural foods. With the aid of models it becomes possible to control and predict quality attributes, so that product innovation can be done more efficiently. However, quality is an elusive concept, and there is always an aspect of subjectivity and uncertainty.
A novel approach in the agro-food chain would be to tackle subjective elements and uncertainty in modelling by using Bayesian statistics and Bayesian Belief Networks. Bayesian approaches use prior probabilities (partly accounting for subjectivity) to estimate posterior probabilities, resulting in higher accuracy than is possible with classical statistical techniques. Thus, the variability and uncertainty in data and decisions, inherent in a complex food chain, can be dealt with.